# Dissecting drug resistance in serial uveal melanoma biopsies using integrated, multi-modal single-cell profiling and novel machine learning tools.

> **NIH NIH R21** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2021 · $189,338

## Abstract

PROJECT SUMMARY
Uveal melanoma (UM) is a rare melanoma subtype with an estimated annual incidence of approximately 2000
in the United States. While most patients have excellent rates of local disease control with surgery or
radiotherapy, nearly half develop metastatic disease, most frequently to the liver. Metastatic UM (MUM) is very
treatment resistant and shows no significant responses to conventional chemotherapies or immune checkpoint
inhibitors (ICI). UM is molecularly characterized by canonical mutations of the Gα protein subunits (GNAQ/11),
which result in hyperactivation of the MAPK pathway. Targeting this pathway with MEK inhibitors (MEKi) results
in significant anti-tumor activity in vitro, and response rates of up to 14% in patients with MUM, thereby exhibiting
significantly higher activity compared to other available systemic therapies. However, there remains significant
potential to improve the efficacy of MEKi. To better define modifiers of MEKi sensitivity and resistance, it is
important to consider the fact that most UM harbor mutually exclusive GNAQ/11co-mutations, including
inactivating mutations or bi-allelic loss of BAP1 (~33%) or deleterious mutations in SF3B1 (~23%) or EIF1AX
(13%), thus define distinct genomic subtypes of UM. These alterations likely provide dependencies that are not
abrogated with MEKi alone, yet they may represent synthetic lethal vulnerabilities in the context of MEKi.
Furthermore, there has not been a systematic evaluation of how MEKi (or any other therapy) alters cancer cell
autonomous and cell non-autonomous mechanisms that could confer drug resistance. This is in part due to
technical barriers and lack of in vivo models that faithfully recapitulate human MUM. In this proposal, we build
on several innovations to systematically determine the impact of MEKi on the MUM ecosystem and define
synthetic lethal dependencies across the UM genomic landscape and in the context of MEKi. We will achieve
this in two specifim aims: In Aim 1, we will perform single-nuclei RNA-sequencing (snRNA-seq) in patients with
MUM who underwent therapy with MEKi selumetinib and had serial biopsies (pre-, on- and off-therapy), and
analyze these with several established analytical methods. Second, building on recent developments, we will
build machine learning tools for the analysis of sequential single-cell data sets. In Aim 2, we will perform patient-
informed CRISPR-screens with multi-modal single-cell RNA/protein readouts across the genomic spectrum of
UM. Finally, we will perform genome-scale CRISPR-screens across multiple models to define genotype-shared
and -unique modifiers of MEKi responses. Together, these approaches will provide the a comprehensive
sequential single-cell analysis in solid tumors, develop tools for temporal single-cell analyses that can be
referenced against a ground truth, and define genotype-dependent synthetic lethal vulnerabilities with concurrent
MEKi therapy.

## Key facts

- **NIH application ID:** 10290692
- **Project number:** 1R21CA263381-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Benjamin Izar
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $189,338
- **Award type:** 1
- **Project period:** 2021-07-08 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10290692

## Citation

> US National Institutes of Health, RePORTER application 10290692, Dissecting drug resistance in serial uveal melanoma biopsies using integrated, multi-modal single-cell profiling and novel machine learning tools. (1R21CA263381-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10290692. Licensed CC0.

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